Chainer supports a common interface for training and validation of datasets. The dataset support consists of three components: datasets, iterators, and batch conversion functions.

Dataset represents a set of examples. The interface is only determined by combination with iterators you want to use on it. The built-in iterators of Chainer require the dataset to support __getitem__ and __len__ methods. In particular, the __getitem__ method should support indexing by both an integer and a slice. We can easily support slice indexing by inheriting DatasetMixin, in which case users only have to implement get_example() method for indexing. Basically, datasets are considered as stateless objects, so that we do not need to save the dataset as a checkpoint of the training procedure.

Iterator iterates over the dataset, and at each iteration, it yields a mini-batch of examples as a list. Iterators should support the Iterator interface, which includes the standard iterator protocol of Python. Iterators manage where to read next, which means they are stateful.

Batch conversion function converts the mini-batch into arrays to feed to the neural nets. They are also responsible to send each array to an appropriate device.
Chainer currently provides two implementations:

These components are all customizable, and designed to have a minimum interface to restrict the types of datasets and ways to handle them. In most cases, though, implementations provided by Chainer itself are enough to cover the usages.

Chainer also has a light system to download, manage, and cache concrete examples of datasets. All datasets managed through the system are saved under the dataset root directory, which is determined by the CHAINER_DATASET_ROOT environment variable, and can also be set by the set_dataset_root() function.

The most basic dataset implementation is an array.
Both NumPy and CuPy arrays can be used directly as datasets.

In many cases, though, the simple arrays are not enough to write the training procedure.
In order to cover most of such cases, Chainer provides many built-in implementations of datasets.

These built-in datasets are divided into two groups.
One is a group of general datasets.
Most of them are wrapper of other datasets to introduce some structures (e.g., tuple or dict) to each data point.
The other one is a group of concrete, popular datasets.
These concrete examples use the downloading utilities in the chainer.dataset module to cache downloaded and converted datasets.

The first one is DictDataset and TupleDataset, both of which combine other datasets and introduce some structures on them.

The second one is ConcatenatedDataset and SubDataset.
ConcatenatedDataset represents a concatenation of existing datasets. It can be used to merge datasets and make a larger dataset.
SubDataset represents a subset of an existing dataset. It can be used to separate a dataset for hold-out validation or cross validation. Convenient functions to make random splits are also provided.

The third one is TransformDataset, which wraps around a dataset by applying a function to data indexed from the underlying dataset.
It can be used to modify behavior of a dataset that is already prepared.